Use of population viability analysis and reserve selection algorithms in regional conservation plans

Current reserve selection algorithms have difficulty evaluating connectivity and other factors necessary to conserve wide-ranging species in developing landscapes. Conversely, population viability analyses may incorporate detailed demographic data, but often lack sufficient spatial detail or are limited to too few taxa to be relevant to regional conservation plans. We developed a regional conservation plan for mammalian carnivores in the Rocky Mountain region using both a reserve selection algorithm (SITES) and, a spatially explicit population model (PATCH). The spatially explicit population model informed reserve selection and network design by producing data on the locations of population sources, the degree of threat to those areas from landscape change, the existence of thresholds to population viability as the size of the reserve network increased, and the effect of linkage areas on population persistence. A 15% regional decline in carrying capacity for large carnivores was predicted within 25 years if no addition to protected areas occurred. Increasing the percentage of the region in reserves from the current 17.2% to 36.4% would result in a 1-4% increase over current carrying capacity, despite the effects of landscape change. The population model identified linkage areas that were not chosen by the reserve selection algorithm, but whose protection strongly affected population viability. A reserve network based on carnivore conservation goals incidentally protected 76% of ecosystem types, but was poor at capturing localized rare species. Although it is unlikely that planning for focal species requirements alone will capture all facets of biodiversity, when used in combination with other planning foci, it may help to forestall the effects of loss of connectivity on a larger group of threatened species and ecosystems. A better integration of current reserve selection tools and spatial simulation models should produce reserve designs that are simultaneously biologically realistic and taxonomically inclusive

Genetic factors in threatened species recovery plans on three continents

Around the world, recovery planning for threatened species is being applied in an attempt to stem the current extinction crisis. Genetic factors linked to small population processes (eg inbreeding, loss of genetic diversity) play a key role in species viability. We examined how often genetic factors are considered in threatened species recovery planning. We selected recent species recovery plans from Europe (n = 110), North America (the US only; n = 100), and Australia (n = 108), and reviewed three broad categories of genetic data they address: population-genetic, fitness-related, and life-history data. We found that the host country, taxonomic group to which the species belonged, and several proposed management actions were important predictors of the inclusion of genetic factors. Notably, species recovery plans from the US were more likely to include genetic issues, probably due to legislative requirements. We recommend an international standard, similar to an IUCN Red List framework, that requires explicit consideration of genetic aspects of long-term viability

Habitat connectivity and spotted owl population dynamics

Thesis (Ph. D.)--University of Washington, 1995The ecological consequences of habitat fragmentation include the direct effects of habitat loss and the indirect effects of reduced inter-patch dispersal. I examine these consequences of habitat fragmentation through two separate studies. The first study measures habitat connectivity through the success of a simulated dispersal process, and then asks if connectivity can be estimated from measures of habitat pattern alone. The second study examines the effect of past habitat loss, and of potential future habitat gains, on an isolated population of northern spotted owls on the Olympic Peninsula of Washington state.Indices of landscape pattern are frequently used to estimate habitat connectivity, but whether they actually do so remains undocumented. If indices of habitat pattern do indeed estimate habitat connectivity, then these indices should correlate well with predictions of dispersal success. To test this possibility, I looked for correlations between nine common indices of habitat pattern and the results of a simulated dispersal process conducted using GIS data for old-growth forest throughout the Pacific Northwest. The nine indices of habitat pattern that I examined were only weakly correlated with the results from the dispersal modeling, but I identified a new pattern index, termed patch cohesion, for which the fit was much better.I constructed a spatially explicit simulation model for the population of spotted owls on the Olympic Peninsula of Washington state, and used this model to examine the future of this population, and how patterns of occupancy might differ in habitats of varying quality. Results from model simulations that incorporate habitat loss over the last three quarters of a century lend support to suspicions that the population of spotted owls on the Olympic Peninsula may presently be in a sharp decline. But simulations that account for regeneration of owl habitat over the next 100 years show that the potential exists for this trend to reverse, and for the population to stabilize. The simulated owl population showed a two-fold response to the addition of new habitat over what would have been expected based on estimates of habitat area alone

Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA

We selected the COVID-19 outbreak in the state of Oregon, USA as a system for developing a general geographically nuanced epidemiological forecasting model that balances simplicity, realism, and accessibility. Using the life history simulator HexSim, we inserted a mathematical SIRD disease model into a spatially explicit framework, creating a distributed array of linked compartment models. Our spatial model introduced few additional parameters, but casting the SIRD equations into a geographic setting significantly altered the system’s emergent dynamics. Relative to the non-spatial model, our simple spatial model better replicated the record of observed infection rates in Oregon. We also observed that estimates of vaccination efficacy drawn from the non-spatial model tended to be higher than those obtained from models that incorporate geographic variation. Our spatially explicit SIRD simulations of COVID-19 in Oregon suggest that modest additions of spatial complexity can bring considerable realism to a traditional disease model

Data from: Intrinsic and extrinsic drivers of source-sink dynamics

Many factors affect the presence and exchange of individuals among subpopulations and influence not only the emergence, but the strength of ensuing source–sink dynamics within metapopulations. Yet their relative contributions remain largely unexplored. To help identify the characteristics of empirical systems that are likely to exhibit strong versus weak source–sink dynamics and inform their differential management, we compared the relative roles of influential factors in strengthening source–sink dynamics. In a series of controlled experiments within a spatially explicit individual-based model framework, we varied patch quality, patch size, the dispersion of high- and low-quality patches, population growth rates, dispersal distances, and environmental stochasticity in a factorial design. We then recorded source–sink dynamics that emerged from the simulated habitat and population factors. Long-term differences in births and deaths were quantified for sources and sinks in each system and used in a statistical model to rank the influences of key factors. Our results suggest that systems with species capable of rapid growth, occupying habitat patches with more disparate qualities, with interspersed higher- and lower-quality habitats, and that experience relatively stable environments (i.e., fewer negative perturbations) are more likely to exhibit strong source–sink dynamics. Strong source–sink dynamics emerged under diverse combinations of factors, suggesting that simple inferences of process from pattern will likely be inadequate to predict and assess the strength of source–sink dynamics. Our results also suggest that it may be more difficult to detect and accurately measure source–sink dynamics in slow-growing populations, highly variable environments, and where a subtle gradient of habitat quality exists

Extinction Debt Of Protected Areas In Developing Landscapes

To conserve biological diversity, protected-area networks must be based not only on current species distributions but also on the landscape\u27s long-term capacity to support populations. We used spatially explicit population models requiring detailed habitat and demographic data to evaluate the ability of existing park systems in the Rocky Mountain region (U.S.A. and Canada) to sustain populations of mammalian carnivores. Predicted patterns of extirpation agreed with those from logistic-regression models based only on park size and connectedness (or isolation) for the grizzly bear (Ursus arctos) in developed landscapes (northern U.S. Rocky Mountains) and semideveloped landscapes (southern Canadian Rocky Mountains). The area-isolation model performed poorly where the landscape matrix contained large amounts of suitable habitat (northern Canadian Rocky Mountains). Park area and connectedness were poor predictors of gray wolf (Canis lupus) occurrence because of this species\u27 broader-scale range dynamics and greater ability to inhabit the landscape matrix. A doubling of park area corresponded to a 47% and 57% increase in projected grizzly bear population persistence in developed and semideveloped landscapes, respectively. A doubling of a park\u27s connectedness index corresponded to a 81% and 350% increase in population persistence in developed and semideveloped landscapes, respectively, suggesting that conservation planning to enhance connectivity may be most effective in the earliest stages of landscape degradation. The park area and connectivity required for population persistence increased as the landscape matrix became more hostile, implying that the relatively small combined area of parks in the boreal forest and other undeveloped regions may fall below the threshold for species persistence if parks become habitat islands. Loss of carnivores from boreal landscapes could further reduce the viability of temperate populations occupying refugia at the southern range margin. Spatially realistic population models may be more informative than simpler patch-matrix models in predicting the effects of landscape change on population viability across a continuum of landscape degradation

Adding pattern and process to eco-evo theory and applications

Eco-evolutionary dynamics result when interacting biological forces simultaneously produce demographic and genetic population responses. Eco-evolutionary simulators traditionally manage complexity by minimizing the influence of spatial pattern on process. However, such simplifications can limit their utility in real-world applications. We present a novel simulation modeling approach for investigating eco-evolutionary dynamics, centered on the driving role of landscape pattern. Our spatially-explicit, individual-based mechanistic simulation approach overcomes existing methodological challenges, generates new insights, and paves the way for future investigations in four focal disciplines: Landscape Genetics, Population Genetics, Conservation Biology, and Evolutionary Ecology. We developed a simple individual-based model to illustrate how spatial structure drives eco-evo dynamics. By making minor changes to our landscape’s structure, we simulated continuous, isolated, and semi-connected landscapes, and simultaneously tested several classical assumptions of the focal disciplines. Our results exhibit expected patterns of isolation, drift, and extinction. By imposing landscape change on otherwise functionally-static eco-evolutionary models, we altered key emergent properties such as gene-flow and adaptive selection. We observed demo-genetic responses to these landscape manipulations, including changes in population size, probability of extinction, and allele frequencies. Our model also demonstrated how demo-genetic traits, including generation time and migration rate, can arise from a mechanistic model, rather than being specified a priori. We identify simplifying assumptions common to four focal disciplines, and illustrate how new insights might be developed in eco-evolutionary theory and applications by better linking biological processes to landscape patterns that we know influence them, but that have understandably been left out of many past modeling studies

Adding pattern and process to eco-evo theory and applications.

Eco-evolutionary dynamics result when interacting biological forces simultaneously produce demographic and genetic population responses. Eco-evolutionary simulators traditionally manage complexity by minimizing the influence of spatial pattern on process. However, such simplifications can limit their utility in real-world applications. We present a novel simulation modeling approach for investigating eco-evolutionary dynamics, centered on the driving role of landscape pattern. Our spatially-explicit, individual-based mechanistic simulation approach overcomes existing methodological challenges, generates new insights, and paves the way for future investigations in four focal disciplines: Landscape Genetics, Population Genetics, Conservation Biology, and Evolutionary Ecology. We developed a simple individual-based model to illustrate how spatial structure drives eco-evo dynamics. By making minor changes to our landscape's structure, we simulated continuous, isolated, and semi-connected landscapes, and simultaneously tested several classical assumptions of the focal disciplines. Our results exhibit expected patterns of isolation, drift, and extinction. By imposing landscape change on otherwise functionally-static eco-evolutionary models, we altered key emergent properties such as gene-flow and adaptive selection. We observed demo-genetic responses to these landscape manipulations, including changes in population size, probability of extinction, and allele frequencies. Our model also demonstrated how demo-genetic traits, including generation time and migration rate, can arise from a mechanistic model, rather than being specified a priori. We identify simplifying assumptions common to four focal disciplines, and illustrate how new insights might be developed in eco-evolutionary theory and applications by better linking biological processes to landscape patterns that we know influence them, but that have understandably been left out of many past modeling studies